Scientific collaboration, development milestones and perspectives on the forces shaping natural medicines and AI-enabled research.
Collaboration with tertiary hospitals connects hospital preparations and clinical needs with translation and investigator-initiated research, bringing human experience into CMC, pharmacology and clinical questions.
Explore translation capabilities ↗Veritas initiated AI-driven research and development of China Class 1 innovative TCM drugs, connecting intelligent formulation, mechanism studies and preclinical validation within a concrete research program.
Explore VMED-OS applications ↗The agencies jointly set out ten good AI practice principles covering context of use, data governance, risk-based assessment and lifecycle management.
Partner evaluation should connect model performance with specific decisions: why a candidate enters an experiment, which endpoint tests it, and how negative results inform the next cycle. Integrated computation and experiments can create an inspectable chain from prediction to evidence to action.
Read the FDA principles ↗FDA’s January 2025 draft guidance proposes a risk-based framework for evaluating AI credibility in a defined context of use. The document remains draft guidance.
Indication prioritization, formulation design and quality assessment are distinct tasks. Each needs appropriate data, experimental endpoints and evaluation criteria. Defining the intended research use is the starting point for validation that can influence development.
Read the FDA draft guidance ↗